Image of PENGGUNAAN METODE YOLO UNTUK DETEKSI KENDARAAN DAN PENENTUAN TINGKAT PELANGGARAN MELAWAN ARUS LALU LINTAS MENGGUNAKAN ALGORITMA ONE DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK PADA JALAN RAYA KOTA PALEMBANG

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PENGGUNAAN METODE YOLO UNTUK DETEKSI KENDARAAN DAN PENENTUAN TINGKAT PELANGGARAN MELAWAN ARUS LALU LINTAS MENGGUNAKAN ALGORITMA ONE DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK PADA JALAN RAYA KOTA PALEMBANG

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Wrong-way traffic violation is often committed by the public and frequently leads to traffic accidents and congestion. This research aims to develop a detection system using the You Only Look Once (YOLO) algorithm to identify and count the number of vehicles based on video recordings. Additionally, this study employs the One-Dimensional Convolutional Neural Network (1DCNN) method to determine the violation level. The dataset consists of 3592 images and 40 videos of motorcycles and cars, along with a reference table of violation levels with 3 columns and 16 rows. The YOLO model achieves a model accuracy of 74.66%, while the accuracy for testing data image readings is 70.13%. The average accuracy for video data readings is 99.34%. The 1DCNN model produces a model accuracy of 50% and reading accuracy of 90%. In this study, it is found that the YOLO model can process video data to detect vehicles, the 1DCNN model can be applied to determine the wrong-way violation, and the output of the violation level in this research has three conditions: "few," "moderate," and "many," applied to the analysis of 40 videos. Based on the results obtained, it can be predicted that Srijaya Negara Street in front of the Sriwijaya University Palembang and H.M. Noerdin Street will experience many violations against traffic direction based on the analyzed violation levels.

Availability
Inventory Code Barcode Call Number Location Status
2407000294T137763T1377632024Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1377632024
Publisher
Inderalaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Uniersitas Sriwijaya.,
Collation
xiii, 55 hlm.; Ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
006.307
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Sistem Pakar
Prodi Sistem Komputer
Specific Detail Info
-
Statement of Responsibility
Other version/related

No other version available

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